



import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel







def  lowerCAmelCase_	(lowerCAmelCase__:		int=3_2		,						lowerCAmelCase__:		Optional[int]=1_0		,						lowerCAmelCase__:		List[Any]=1_0_0		,						lowerCAmelCase__:		Tuple=1_0_2_6		,						lowerCAmelCase__:		Optional[Any]=True		,						lowerCAmelCase__:		str="data/tokenized_stories_train_wikitext103.jbl"		,						lowerCAmelCase__:		str="igf_context_pairs.jbl"		,						):





			"""simple docstring"""


			set_seed(3  )
			# generate train_data and objective_set
			UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     generate_datasets(
			    lowerCAmelCase__		,						lowerCAmelCase__		,						number=lowerCAmelCase__		,						min_len=1_0_2_6		,						trim=lowerCAmelCase__  )
			# keeps model same across runs
			set_seed(4  )
			# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
			# can we train on GPU?
			UpperCAmelCase_:		Any							      =     torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""  )

			# load pretrained model
			UpperCAmelCase_:		int							      =     load_gpta("""gpt2"""  ).to(lowerCAmelCase__  )
			print("""computing perplexity on objective set"""  )
			UpperCAmelCase_:		List[Any]							      =     compute_perplexity(lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__  ).item()
			print("""perplexity on objective set:"""		,						lowerCAmelCase__  )

			# collect igf pairs and save to file demo.jbl
			collect_objective_set(lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__  )

			# clean up, delete model and data we don't need anymore
			del model, train_data, objective_set
			torch.cuda.empty_cache()







def  lowerCAmelCase_	(lowerCAmelCase__:		Tuple		,						lowerCAmelCase__:		Optional[Any]=1_5		,						lowerCAmelCase__:		Tuple=1_2_8		,						lowerCAmelCase__:		Union[str, Any]=1_0_0		,						lowerCAmelCase__:		Optional[Any]="igf_model.pt"		,						):





			"""simple docstring"""


			set_seed(4_2  )

			# Load pre-trained model
			UpperCAmelCase_:		Tuple							      =     GPTaLMHeadModel.from_pretrained("""gpt2"""  )

			# Initialize secondary learner to use embedding weights of model
			UpperCAmelCase_:		Tuple							      =     SecondaryLearner(lowerCAmelCase__  )

			# Train secondary learner
			UpperCAmelCase_:		str							      =     train_secondary_learner(
			    lowerCAmelCase__		,						lowerCAmelCase__		,						max_epochs=lowerCAmelCase__		,						batch_size=lowerCAmelCase__		,						eval_freq=1_0_0		,						igf_model_path=lowerCAmelCase__		,						)

			del model, secondary_learner_train_data
			torch.cuda.empty_cache()

			return secondary_learner







def  lowerCAmelCase_	(lowerCAmelCase__:		Optional[Any]		,						lowerCAmelCase__:		Any		,						lowerCAmelCase__:		Tuple		,						lowerCAmelCase__:		Dict=3_2		,						lowerCAmelCase__:		Union[str, Any]=1_0_0_0		,						lowerCAmelCase__:		Union[str, Any]=1_6		,						lowerCAmelCase__:		Dict=1.0		,						lowerCAmelCase__:		Any=recopy_gpta		,						lowerCAmelCase__:		Tuple=None		,						lowerCAmelCase__:		Union[str, Any]=1_0		,						lowerCAmelCase__:		Optional[Any]="gpt2_finetuned.pt"		,						):





			"""simple docstring"""


			UpperCAmelCase_:		List[Any]							      =     torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""  )
			UpperCAmelCase_:		int							      =     RandomSampler(lowerCAmelCase__  )
			UpperCAmelCase_:		Any							      =     DataLoader(lowerCAmelCase__		,						sampler=lowerCAmelCase__  )

			UpperCAmelCase_:		Any							      =     max_steps // (len(lowerCAmelCase__  )) + 1
			UpperCAmelCase_:		int							      =     0
			UpperCAmelCase_:		List[str]							      =     torch.zeros((1, context_len)		,						dtype=torch.long		,						device=lowerCAmelCase__  )
			UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_:		int							      =     recopy_model(lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__  )

			model.train()
			if secondary_learner is not None:
						secondary_learner.to(lowerCAmelCase__  )
						secondary_learner.eval()
			UpperCAmelCase_:		Union[str, Any]							      =     []
			UpperCAmelCase_:		Union[str, Any]							      =     0

			UpperCAmelCase_:		Optional[Any]							      =     []
			UpperCAmelCase_:		Union[str, Any]							      =     []

			# Compute the performance of the transformer model at the beginning
			UpperCAmelCase_:		Optional[Any]							      =     compute_perplexity(lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__  )
			test_perps.append(lowerCAmelCase__  )
			print("""Test perplexity, step"""		,						lowerCAmelCase__		,						""":"""		,						lowerCAmelCase__  )
			for epoch in range(int(lowerCAmelCase__  )  ):
						for step, example in enumerate(lowerCAmelCase__  ):
									torch.cuda.empty_cache()
									UpperCAmelCase_:		List[Any]							      =     random.randint(0		,						example.size(2  ) - context_len - 1  )
									UpperCAmelCase_:		List[str]							      =     example[0, 0, start : start + context_len]
									lm_optimizer.zero_grad()
									UpperCAmelCase_:		Any							      =     model(lowerCAmelCase__		,						labels=lowerCAmelCase__  )
									UpperCAmelCase_:		List[Any]							      =     True

									if secondary_learner is not None:
												UpperCAmelCase_:		Any							      =     secondary_learner.forward(
												    torch.tensor(lowerCAmelCase__		,						dtype=torch.long		,						device=lowerCAmelCase__  ).unsqueeze(0  )  )[0].item()
												observed_qs.append(float(lowerCAmelCase__  )  )

												# Here we implement the simple non-constant threshold for the predicted IG(X) value
												# We will decay the selectivity of our secondary learner filter from
												# 1 standard deviation above average to 1 below average after 10 batches.

												if global_step == 1_0:
															UpperCAmelCase_:		int							      =     -1
												if predicted_q < threshold:
															UpperCAmelCase_:		int							      =     False

            # If we passed the filter, add the context to the batch!
									if do_backprop:
												contexts.append(np.array(context.cpu()  )  )
												UpperCAmelCase_:		Tuple							      =     outputs[0]
												lm_loss.backward()
												examples += 1

									del outputs

									# Once the batch is filled with enough contexts, backprop on the batch.
									if examples == batch_size:
												torch.cuda.empty_cache()
												UpperCAmelCase_:		Optional[Any]							      =     0
												# Do LM backprop
												torch.nn.utils.clip_grad_norm_(model.parameters()		,						3.0  )
												lm_optimizer.step()
												lm_scheduler.step()  # Update learning rate schedule
												global_step += 1
												# Compute the performance of the transformer model at this batch
												if global_step % eval_interval == 0:
															UpperCAmelCase_:		Any							      =     compute_perplexity(lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__  )
															test_perps.append(lowerCAmelCase__  )

															print("""Test perplexity, step"""		,						lowerCAmelCase__		,						""":"""		,						lowerCAmelCase__  )
            # Break out of the loop after 60 batches
									if max_steps > 0 and global_step > 6_0:
												break
						if max_steps > 0 and global_step > 6_0:
									break

    # save finetuned transformer model
			torch.save(model.state_dict()		,						lowerCAmelCase__  )
			torch.cuda.empty_cache()
			# Do some cleaning up so we can reinitialize for the next run of this function
			del lm_optimizer
			del lm_scheduler
			return model







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		Optional[Any]							      =     argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task"""  )

			# Required parameters
			parser.add_argument(
			    """--data_dir"""		,						default=lowerCAmelCase__		,						type=lowerCAmelCase__		,						required=lowerCAmelCase__		,						help="""The input data dir. Should contain data files for WikiText."""		,						)
			parser.add_argument(
			    """--model_name_or_path"""		,						default=lowerCAmelCase__		,						type=lowerCAmelCase__		,						required=lowerCAmelCase__		,						help="""Path to pretrained model or model identifier from huggingface.co/models"""		,						)
			parser.add_argument(
			    """--data_file"""		,						type=lowerCAmelCase__		,						default=lowerCAmelCase__		,						help=(
			        """A jbl file containing tokenized data which can be split as objective dataset, """
			        """train_dataset and test_dataset."""
			    )		,						)

			parser.add_argument(
			    """--igf_data_file"""		,						type=lowerCAmelCase__		,						default=lowerCAmelCase__		,						help="""A jbl file containing the context and information gain pairs to train secondary learner."""		,						)

			parser.add_argument(
			    """--output_dir"""		,						default=lowerCAmelCase__		,						type=lowerCAmelCase__		,						required=lowerCAmelCase__		,						help="""The output directory where the final fine-tuned model is stored."""		,						)

			parser.add_argument(
			    """--tokenizer_name"""		,						default=lowerCAmelCase__		,						type=lowerCAmelCase__		,						help="""Pretrained tokenizer name or path if not the same as model_name"""		,						)
			parser.add_argument("""--seed"""		,						type=lowerCAmelCase__		,						default=lowerCAmelCase__		,						help="""A seed for reproducible training."""  )

			parser.add_argument(
			    """--context_len"""		,						default=3_2		,						type=lowerCAmelCase__		,						help=(
			        """The maximum total input sequence length after tokenization. Sequences longer """
			        """than this will be truncated, sequences shorter will be padded."""
			    )		,						)

			parser.add_argument(
			    """--size_objective_set"""		,						default=1_0_0		,						type=lowerCAmelCase__		,						help="""number of articles that are long enough to be used as our objective set"""		,						)
			parser.add_argument(
			    """--eval_freq"""		,						default=1_0_0		,						type=lowerCAmelCase__		,						help="""secondary model evaluation is triggered at eval_freq"""  )

			parser.add_argument("""--max_steps"""		,						default=1_0_0_0		,						type=lowerCAmelCase__		,						help="""To calculate training epochs"""  )

			parser.add_argument(
			    """--secondary_learner_batch_size"""		,						default=1_2_8		,						type=lowerCAmelCase__		,						help="""batch size of training data for secondary learner"""		,						)

			parser.add_argument(
			    """--batch_size"""		,						default=1_6		,						type=lowerCAmelCase__		,						help="""batch size of training data of language model(gpt2) """  )

			parser.add_argument(
			    """--eval_interval"""		,						default=1_0		,						type=lowerCAmelCase__		,						help=(
			        """decay the selectivity of our secondary learner filter from"""
			        """1 standard deviation above average to 1 below average after 10 batches"""
			    )		,						)

			parser.add_argument(
			    """--number"""		,						default=1_0_0		,						type=lowerCAmelCase__		,						help="""The number of examples split to be used as objective_set/test_data"""  )

			parser.add_argument(
			    """--min_len"""		,						default=1_0_2_6		,						type=lowerCAmelCase__		,						help="""The minimum length of the article to be used as objective set"""  )

			parser.add_argument(
			    """--secondary_learner_max_epochs"""		,						default=1_5		,						type=lowerCAmelCase__		,						help="""number of epochs to train secondary learner"""  )

			parser.add_argument("""--trim"""		,						default=lowerCAmelCase__		,						type=lowerCAmelCase__		,						help="""truncate the example if it exceeds context length"""  )

			parser.add_argument(
			    """--threshold"""		,						default=1.0		,						type=lowerCAmelCase__		,						help=(
			        """The threshold value used by secondary learner to filter the train_data and allow only"""
			        """ informative data as input to the model"""
			    )		,						)

			parser.add_argument("""--finetuned_model_name"""		,						default="""gpt2_finetuned.pt"""		,						type=lowerCAmelCase__		,						help="""finetuned_model_name"""  )

			parser.add_argument(
			    """--recopy_model"""		,						default=lowerCAmelCase__		,						type=lowerCAmelCase__		,						help="""Reset the model to the original pretrained GPT-2 weights after each iteration"""		,						)

			# function calls
			# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
			generate_n_pairs(
			    context_len=3_2		,						max_steps=1_0		,						size_objective_set=1_0_0		,						min_len=1_0_2_6		,						trim=lowerCAmelCase__		,						data_file="""data/tokenized_stories_train_wikitext103.jbl"""		,						igf_data_file="""igf_context_pairs.jbl"""		,						)

			# Load train data for secondary learner
			UpperCAmelCase_:		Union[str, Any]							      =     joblib.load("""data/IGF_values.jbl"""  )

			# Train secondary learner
			UpperCAmelCase_:		Dict							      =     training_secondary_learner(
			    lowerCAmelCase__		,						secondary_learner_max_epochs=1_5		,						secondary_learner_batch_size=1_2_8		,						eval_freq=1_0_0		,						igf_model_path="""igf_model.pt"""		,						)

			# load pretrained gpt2 model
			UpperCAmelCase_:		Dict							      =     GPTaLMHeadModel.from_pretrained("""gpt2"""  )
			set_seed(4_2  )

			# Generate train and test data to train and evaluate gpt2 model
			UpperCAmelCase_      ,							UpperCAmelCase_:		Optional[Any]							      =     generate_datasets(
			    context_len=3_2		,						file="""data/tokenized_stories_train_wikitext103.jbl"""		,						number=1_0_0		,						min_len=1_0_2_6		,						trim=lowerCAmelCase__  )

			# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
			finetune(
			    lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						context_len=3_2		,						max_steps=1_0_0_0		,						batch_size=1_6		,						threshold=1.0		,						recopy_model=lowerCAmelCase__		,						secondary_learner=lowerCAmelCase__		,						eval_interval=1_0		,						finetuned_model_name="""gpt2_finetuned.pt"""		,						)


if __name__ == "__main__":
	main()











import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


a      :  Optional[Any]			     =			logging.getLogger(__name__)







@dataclass(frozen=_lowerCAmelCase					)
class 				_a				:
			A      		=	42
			A      		=	42
			A      		=	None
			A      		=	None
			A      		=	None







@dataclass(frozen=_lowerCAmelCase					)
class 				_a				:
			A      		=	42
			A      		=	None
			A      		=	None
			A      		=	None
			A      		=	None


if is_torch_available():
	import torch
	from torch.utils.data import Dataset







	class 				_a				(   _lowerCAmelCase					):
				A      		=	42

				def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_ = False,   )       ->   Union[str, Any]:
							UpperCAmelCase_:		Optional[Any]							      =     hans_processors[task]()

							UpperCAmelCase_:		Dict							      =     os.path.join(
							    SCREAMING_SNAKE_CASE_,   """cached_{}_{}_{}_{}""".format(
							        """dev""" if evaluate else """train""",   tokenizer.__class__.__name__,   str(SCREAMING_SNAKE_CASE_					),   SCREAMING_SNAKE_CASE_,   ),   )
							UpperCAmelCase_:		Dict							      =     processor.get_labels()
							if tokenizer.__class__ in (
							    RobertaTokenizer,
							    RobertaTokenizerFast,
							    XLMRobertaTokenizer,
							    BartTokenizer,
							    BartTokenizerFast,
							):
										# HACK(label indices are swapped in RoBERTa pretrained model)
										UpperCAmelCase_      ,							UpperCAmelCase_:		Tuple							      =     label_list[2], label_list[1]
							UpperCAmelCase_:		str							      =     label_list

							# Make sure only the first process in distributed training processes the dataset,
							# and the others will use the cache.
							UpperCAmelCase_:		int							      =     cached_features_file + """.lock"""
							with FileLock(SCREAMING_SNAKE_CASE_					):
										if os.path.exists(SCREAMING_SNAKE_CASE_					) and not overwrite_cache:
													logger.info(f'Loading features from cached file {cached_features_file}'					)
													UpperCAmelCase_:		Dict							      =     torch.load(SCREAMING_SNAKE_CASE_					)
										else:
													logger.info(f'Creating features from dataset file at {data_dir}'					)

													UpperCAmelCase_:		Union[str, Any]							      =     (
													    processor.get_dev_examples(SCREAMING_SNAKE_CASE_					) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_					)
													)

													logger.info("""Training examples: %s""",   len(SCREAMING_SNAKE_CASE_					)					)
													UpperCAmelCase_:		List[Any]							      =     hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
													logger.info("""Saving features into cached file %s""",   SCREAMING_SNAKE_CASE_					)
													torch.save(self.features,   SCREAMING_SNAKE_CASE_					)

				def __len__(self					)       ->   Optional[int]:
							return len(self.features					)

				def __getitem__(self,   SCREAMING_SNAKE_CASE_					)       ->   InputFeatures:
							return self.features[i]

				def      __snake_case       (self					)       ->   List[str]:
							return self.label_list


if is_tf_available():
	import tensorflow as tf







	class 				_a				:
				A      		=	42

				def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = 128,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_ = False,   )       ->   Optional[int]:
							UpperCAmelCase_:		Union[str, Any]							      =     hans_processors[task]()
							UpperCAmelCase_:		List[Any]							      =     processor.get_labels()
							if tokenizer.__class__ in (
							    RobertaTokenizer,
							    RobertaTokenizerFast,
							    XLMRobertaTokenizer,
							    BartTokenizer,
							    BartTokenizerFast,
							):
										# HACK(label indices are swapped in RoBERTa pretrained model)
										UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     label_list[2], label_list[1]
							UpperCAmelCase_:		Dict							      =     label_list

							UpperCAmelCase_:		List[str]							      =     processor.get_dev_examples(SCREAMING_SNAKE_CASE_					) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_					)
							UpperCAmelCase_:		Any							      =     hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

							def gen():
										for ex_index, ex in tqdm.tqdm(enumerate(self.features					),   desc="""convert examples to features"""					):
													if ex_index % 10000 == 0:
																logger.info("""Writing example %d of %d""" % (ex_index, len(SCREAMING_SNAKE_CASE_					))					)

													yield (
													    {
													        "example_id": 0,
													        "input_ids": ex.input_ids,
													        "attention_mask": ex.attention_mask,
													        "token_type_ids": ex.token_type_ids,
													    },
													    ex.label,
													)

							UpperCAmelCase_:		List[str]							      =     tf.data.Dataset.from_generator(
							    SCREAMING_SNAKE_CASE_,   (
							        {
							            """example_id""": tf.intaa,
							            """input_ids""": tf.intaa,
							            """attention_mask""": tf.intaa,
							            """token_type_ids""": tf.intaa,
							        },
							        tf.intaa,
							    ),   (
							        {
							            """example_id""": tf.TensorShape([]					),
							            """input_ids""": tf.TensorShape([None, None]					),
							            """attention_mask""": tf.TensorShape([None, None]					),
							            """token_type_ids""": tf.TensorShape([None, None]					),
							        },
							        tf.TensorShape([]					),
							    ),   )

				def      __snake_case       (self					)       ->   Optional[Any]:
							return self.dataset

				def __len__(self					)       ->   Optional[int]:
							return len(self.features					)

				def __getitem__(self,   SCREAMING_SNAKE_CASE_					)       ->   InputFeatures:
							return self.features[i]

				def      __snake_case       (self					)       ->   Union[str, Any]:
							return self.label_list







class 				_a				(   _lowerCAmelCase					):

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Dict:
						return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_,   """heuristics_train_set.txt"""					)					),   """train"""					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Dict:
						return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_,   """heuristics_evaluation_set.txt"""					)					),   """dev"""					)

			def      __snake_case       (self					)       ->   Optional[int]:
						return ["contradiction", "entailment", "neutral"]

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
						UpperCAmelCase_:		Optional[Any]							      =     []
						for i, line in enumerate(SCREAMING_SNAKE_CASE_					):
									if i == 0:
												continue
									UpperCAmelCase_:		Tuple							      =     """%s-%s""" % (set_type, line[0])
									UpperCAmelCase_:		int							      =     line[5]
									UpperCAmelCase_:		int							      =     line[6]
									UpperCAmelCase_:		List[str]							      =     line[7][2:] if line[7].startswith("""ex"""					) else line[7]
									UpperCAmelCase_:		Any							      =     line[0]
									examples.append(InputExample(guid=SCREAMING_SNAKE_CASE_,   text_a=SCREAMING_SNAKE_CASE_,   text_b=SCREAMING_SNAKE_CASE_,   label=SCREAMING_SNAKE_CASE_,   pairID=SCREAMING_SNAKE_CASE_					)					)
						return examples







def  lowerCAmelCase_	(lowerCAmelCase__:		List[InputExample]		,						lowerCAmelCase__:		List[str]		,						lowerCAmelCase__:		int		,						lowerCAmelCase__:		PreTrainedTokenizer		,						):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]							      =     {label: i for i, label in enumerate(lowerCAmelCase__  )}

			UpperCAmelCase_:		Optional[int]							      =     []
			for ex_index, example in tqdm.tqdm(enumerate(lowerCAmelCase__  )		,						desc="""convert examples to features"""  ):
						if ex_index % 1_0_0_0_0 == 0:
									logger.info("""Writing example %d""" % (ex_index)  )

						UpperCAmelCase_:		Tuple							      =     tokenizer(
						    example.text_a		,						example.text_b		,						add_special_tokens=lowerCAmelCase__		,						max_length=lowerCAmelCase__		,						padding="""max_length"""		,						truncation=lowerCAmelCase__		,						return_overflowing_tokens=lowerCAmelCase__		,						)

						UpperCAmelCase_:		Optional[Any]							      =     label_map[example.label] if example.label in label_map else 0

						UpperCAmelCase_:		Tuple							      =     int(example.pairID  )

						features.append(InputFeatures(**lowerCAmelCase__		,						label=lowerCAmelCase__		,						pairID=lowerCAmelCase__  )  )

			for i, example in enumerate(examples[:5]  ):
						logger.info("""*** Example ***"""  )
						logger.info(F'guid: {example}'  )
						logger.info(F'features: {features[i]}'  )

			return features


a      :  Optional[int]			     =			{
    'hans': 3,
}

a      :  Dict			     =			{
    'hans': HansProcessor,
}






